The report summarizes new research on the collection, analysis and dissemination of data as related to economic development practices. Researchers conducted surveys of economic developers and data consumers, web scans, and interviews to compile a collection of best practices in data use in the industry. EDOs can use it to better analyze data available to them and present effective data to businesses that consult with them, as well as track the story data tell about communities.

A great deal of the research in the report identifies the changes in data collection and consumption since the publication of the IEDC Data Standards Document in 2002. With the rise of the Internet, data are being created and consumed at increasingly granular levels, and are more easily accessible with the rise of open data initiatives and mobile capabilities. According to the report, EDOs need to seriously consider these changes when providing data to users such as incumbent and potential businesses.

In order to tell an accurate story about a community for purposes of economic development, the report encourages EDOs to ensure their collected and distributed data rises to the new standards. This requires knowledge of all sources, constant collection of new data, and accessible packaging of the data. While consumers can collect data by their own devices today, this paper explores tactics for economic developers to provide the missing link between what users see and an accurate interpretation of what the collected data means.

As a transformative technology, 3D printing has had a long gestation, but its perception as one of the most significant inventions ever is about to become much more widespread. With fast-evolving techniques, applications and printed materials, including metals, 3D printing is becoming a critical tool from prototyping to final production across industries.

This article examines the business trends driving the wider uptake of 3D printing and the technology trends that are enabling it to broaden its scope to less traditional industries. We explore how a range of different sectors are now starting to use 3D printing, including for food production and within resources industries, such as oil and gas.

The case for 3D printing will vary between organizations and sectors: it may be rooted in issues such as pricing pressure, increasing customer requirements or the operational challenges of responding to customer needs quickly enough. Overall, though, 3D printing can help companies to gain competitive advantage, improve their position in the value chain, achieve growth and increase the efficiency of their supply chain and operations.

But slices of that pie were far from equal, according to a report released Thursday from the Congressional Budget Office.

The top 10% of families — those who had at least $942,000 — held 76% of total wealth. The average amount of wealth in this group was $4 million.

Everyone else in the top 50% of the country accounted for 23% of total wealth, with an average of $316,000 per family.

That leaves just 1% of the total pie for the entire bottom half of the population.

The average held was $36,000 for families that fell in the 26th to 50th percentiles. But if they fell in the bottom quarter, they had zero wealth and in fact, were $13,000 in debt on average, CBO found.

Age and education made a difference in wealth accumulation.

Not surprisingly, wealth was higher for households headed by someone 65 or older. Median wealth for these families was $211,000, or almost three-and-a-half times higher than the median for households run by someone 35 to 49.

Families run by adults with college degrees, meanwhile, had a median wealth of $202,000, or nearly four times that of families headed by someone who only had a high school diploma.

What a difference 25 years made

Changes in wealth over time was also very uneven across groups.

Families at the 90th percentile saw their wealth grow by 54% between 1989 and 2013.

Those at the 50th percentile only experienced a 4% rise during the same period.

And those at the 25th percentile actually saw their wealth drop by 6%.

Their idea: to hold a tour targeting a select number of houses that could most easily be turned over and renovated to new owners, with each stop managed by a volunteer “docent” who helps research the home’s history and provides historical visuals. At the end, prospective buyers attend workshops run by experts at the Wilkinsburg Community Development Corporation to learn about how to access incentive programs. If it works, groups like the WCDC can keep the program going at a minimal cost.

Community volunteers act as tour guides and tell visitors when the house was built, who lived in it, etc. They research the homes with help from historical societies, deed offices, and from crowdsourced testimonials from older residents.

Though adjacent Pittsburgh has successfully bounced back from the decline of the steel industry, growth has been slow to come to Wilkinsburg. About 20 percent of the borough’s properties are vacant, costing local government $26 million in annual upkeep. It’s a unique approach that aims to humanize long-neglected eyesores, while at the same time encouraging community building.

“It’s not a loan,” exclaims the Purdue Research Foundation’s Back a Boiler program. Instead, students enrolled in the program agree to pay the university a percentage of their future earnings over a fixed time period.

The program debuted for this school year, and about 100 students have enrolled. Some private lenders have experimented with income-share agreements, but Purdue is the first U.S. university to adopt it (New York Times). Back a Boiler was championed by Purdue President and former Indiana Governor Mitch Daniels (under whose tenure the public-private Indiana Economic Development Corporation was created). The Indianapolis Star provides more detail:

The college sets the repayment amount based on the anticipated earnings of a student’s area of study, with the goal of not exceeding 10 percent of a graduate’s income. As opposed, to the only other option some students face – a private loan where interest can start accruing immediately. If a student is unsuccessful, or doesn’t earn what they anticipated, monthly payments under “Back-a-Boiler” would go down.

Some have questioned whether the program is a good deal for students, arguing it’s “a trade-off between the safety of having payments tied to income, and the risk of a student repaying more if they earn a higher salary.” Also, the program could become insolvent if only low-earning graduates enroll, but the risk in the scenario is shouldered by the program’s investors rather than the students.

We just gained access to the new business dynamic data for Ashtabula County. Business dynamic data allow us to understand job gains and losses by type business operation (new startup, expansion startup, expansion, and move-in). See below for definitions of these business types.

The table above compares job gains (increases in jobs in Ashtabula County) in 2006 and 2015. Note: These are just job gain numbers and not net job numbers. Later we will share data on job losses.

There are some marked differences in job gains in the two years:

In 2006, 6,354 jobs were gained in the county versus 2,645 in 2015

In 2006, businesses with 2-9 and 10-99 employees accounted for the majority of job gains in 2006 and 2015, however job gains were more substantial in 2006 than 2015.

Businesses with 100 or more employees created some jobs in 2006, but no jobs in 2015.

New startup businesses created the largest number of job gains in both 2006 and 2015, but 2006 gains were much larger than those in 2015.

Expansion startups accounted for more job gains in 2015 than 2006.

Expansions accounted for more job gains in 2006 than 2015.

New move-in businesses accounted for a very small number of job gains in both 2006 and 2015.

The job gain data above underscores the positive role of new startups and existing business expansions. Move-ins, or the attraction of new businesses, played a very small role in job gains in both 2006 and 2015.

Definitions:

> News Startups – a business that was not alive and had no location code (FIPS) in prior year,
now has a FIPS code in current year
> Expansion Startups – a business that has a headquarters (HQ) affiliation other than itself that
now has a new FIPS code in current year
> Expansions – a business that shows an increase in jobs from prior year to current year
> Move In – a business that physically relocated from outside the viewed region (based on the
type of region being viewed, state, CBSA, or county) to inside the viewed region from prior to
current year

Ashtabula County resident employment averaged 41,900 in June 2016, about the same level of average employment as 2010. However, employment began to grow in early 2016, a potentially positive sign. The current labor force average was 44,700, about 5,600 lower than 2007. The rate of labor force contraction has slowed since mid-2014 and the labor force had a small increase in early 2016. The unemployment picture has improved over the past several years as the number of unemployed and the unemployment rate has decreased. Unfortunately, the unemployment rate improvement has primarily been due to labor force contraction, rather than employment growth.

The Bureau of Labor Statistics provides monthly estimates of resident employment (16 years & older) & unemployed residents seeking employment. The twelve-month moving average of employment in the county decreased from 46,600 in 2007 to 41,900 in 2010 and has remained close to that level during the national economic recovery. However, the volatile “not seasonally adjusted” employment estimate for June 2016 increased to 42,729, about 200 better than June 2015. The growth in early 2016 may begin to affect average employment if sustained in coming months.

An analysis of the change in employment months showed that the local economy did not have a sustained period of post-recession employment growth or a satisfactory recovery from recessionary job losses. The growth in early 2016 was an encouraging sign of a strengthening economy, but June estimates were weaker than the preceding months.

The labor force includes all persons at least 16 years old with a job or seeking a job, and excludes persons in the military or institutionalized. In Ashtabula County, the labor force has decreased from 50,000 in 2007 to 44,600 in mid 2016, a loss of 5,400. The recession may have accelerated the contraction and the weak recovery led to some discouraged workers who stopped efforts to gain employment. The county’s labor force has been contracting for a decade, but the rate of loss has slowed since late 2014. The labor force grew slightly in preliminary 2016 estimates.

Since 2007, the annual rate of change in the labor force has typically been negative with varying loss rates of -300 to -700. However, the rate of loss has been decreasing since mid-2014 and the labor force expanded slightly in early 2016, possibly due to the mild winter or better economic conditions.

Unemployment includes persons who are currently jobless, available for work and actively seeking for work in the past four weeks. A jobless person not seeking work is not considered part of the labor force and not counted as unemployed. As of June 2016, 2,700 were considered unemployed in Ashtabula County. The post-recession decrease of 4,000 in county unemployment would normally be regarded as a favorable occurrence, but nearly the same number stopped actively seeking work.

Ashtabula County’s unemployment rate has been decreasing since 2010. In June 2016, the average unemployment rate in Ashtabula County for the past 12 months averaged 6.1%, which was below the long-term average rate of 8.6%.

With a labor force of about 45,000, a change of 1% in the number of persons employed or unemployed in Ashtabula County represented a numerical change affecting 450 workers.

Unemployment and unemployment rates may be affected significantly by both hiring and labor force changes. When the unemployment rate drops and the number of unemployed persons decreases, it may be because more persons are hired, or because persons stop seeking work, or both. Since employment has remained virtually unchanged since 2010, the falling unemployment rate in Ashtabula County was largely due to persons leaving the labor force.

The chances of your business surviving past the five-year mark are somewhat better than they used to be, says one economics expert.

According to research and commentary from Dr. Scott Shane, professor of economics and entrepreneurial studies at Case Western Reserve University (and long-time SBT contributor), startup failure rates have declined slightly for employer firms in recent years.

“In 2010, the odds that a business would fail were lower than in 1980,” Shane confirmed in an email to Small Business Trends,.

Shane stated that three factors govern a small business’s survival rate: age, size and industry, in that order.

“Failure rates drop dramatically as firms age,” Shane said. “This is true across all sectors of the economy, all geographic locations and all time periods.”

As to business longevity, size matters, he said. The bigger the company, the less likely it is to fail.

Small Business Survival Report Summaries

The following seven reports, the first by Small Business Trends CEO and publisher Anita Campbell, the next six by Shane all published over an 11-year period, dating from July 2005 to January 2016 paint a more complete picture of the situation. But Campbell’s initial report deals with at what age most small businesses fail.

July 2005: Business Failure Rates Highest in First Two Years

“Across sectors, 66 percent of new establishments were still in existence two years after their birth, and 44 percent were still in existence four years after,” the Bureau’s statistics showed (PDF).

These findings square with Shane’s reports, which follow — survival rates vary by industry. In this case, the education and health services sector showed the highest survival rate while the information technology sector had the lowest.

It should be noted that the report covered the period from March of 1998 to March of 2002 — the height of the dot-com boom.

April 2008: Startup Failure Rates — The REAL Numbers

In his inaugural report, which used Bureau of the Census data produced for the Office of Advocacy of the U.S. Small Business Administration from 1992 to 2002, Shane found that the survival rate for startups dropped precipitously the first year (25 percent) and then fell another 11 percent the second year. Even though it began to level off after that, each year showed further decline. After ten years, only 29 percent of businesses remained.

Shane alluded to the fact that there are “considerable differences” across industry sectors in business failure rates but did not elaborate, saying that he would do so in a later article.

Shane followed up his initial report a month later sharing data from an article by Amy Knaup in Monthly Labor Review, published by the Bureau of Labor Statisics, which looked at the 1998 cohort of new businesses.

“[T]he average start-up in education and health sector is 50 percent more likely than the average start-up in the information industry to live four years,” Shane said.

He added that the industries that have lower initial survival rates tend to continue with those rates every year.

May 2012: Businesses Face High Rates of Infant Mortality

After a several year hiatus, Shane returned in May of 2012 with another report. This time, he used data from the Bureau of Labor Statistics 1994 cohort, which showed the percentage of businesses alive in a given year that failed during the subsequent year.

Jan. 2016: Business Failure Rates Are Declining

Shane’s most recent report — published in January of this year — brought good news: business survival rates are on the rise following the “bust” of the 2008 Great Recession, which brought a spike in business failures.

Referencing Census Bureau statistics, Shane said that business failure rates and the fraction of American employers that go under each year are in long-term decline.

Conclusion

These startup failure rates reports conclude that the chances of your business surviving beyond five years depends on its age, size and industry sector.

While, historically, only half to less than half of companies are still in business after five years, survival rates are slightly better now than in years past, so there is a reason for hope.

Of course, the data is empirical. It fails to take into account intangible qualities such as the entrepreneur’s passion, grit and determination to succeed. While those can’t be measured, they play a critical role nonetheless.